Bounded fuzzy logic control for optimal scheduling of green hydrogen production and revenue maximisation

TL;DR

This paper introduces a Bounded Fuzzy Logic Control system for scheduling green hydrogen production, maximizing revenue by balancing electricity and hydrogen market opportunities amid renewable energy intermittency.

Contribution

The paper presents a novel BFLC approach that effectively schedules green hydrogen production using forecast data, achieving near-optimal revenue performance compared to perfect foresight.

Findings

BFLC achieves within 9% of optimal revenue.

BFLC outperforms steady control, especially during high price variability.

Revenue gains are significant under elevated price conditions.

Abstract

Hydrogen Purchase Agreements (HPAs) guarantee revenue streams that mitigate the financial risks inherent in the long-term production of green hydrogen from renewable energy sources. However, the intermittency of renewable electricity and the availability of parallel revenue opportunities in both the electricity and hydrogen markets complicate the scheduling of green hydrogen production. The scheduling should maximise the total revenue from short-term sales of electricity and hydrogen against the long-term HPA delivery obligations. This challenge is addressed by developing a Bounded Fuzzy Logic Control (BFLC) which determines the daily HPA delivery target based on day-ahead forecasts of electricity and hydrogen prices, as well as wind capacity factors. Subsequently, the daily target is imposed as a constraint in dispatch optimisation which allocates energy and hydrogen flows for each hour of the day. Revenue comparisons over several years demonstrate that the BFLC achieves total annual revenues within 9% of optimal revenues that are based on perfect foresight. The BFLC revenues consistently exceed those of steady control, with the largest differences observed under conditions of elevated price levels and variability. The BFLC provides an effective long-term scheduling of green hydrogen production, enabling realistic revenue quantification that mitigates economic risks without overlooking economically viable projects.